However, previous NF-based methods forcibly transform the distribution of all features into a single distribution (e.g., unit normal distribution), even when the features can have locally distinct semantic information and thus follow different

Many WSAD methods attribute their improvements to novel network architectures or loss functions, based on the authors' understanding of

Instead, we apply powerful machine learning models for one-class (Moya et al., 1993) or

Kullback-Leibler (KL) divergence is one of the most important measures to calculate the difference between probability distributions. In this paper, we theoretically study several properties of KL divergence between multivariate Gaussian distributions.

However, these methods still suffer from an over-generalization issue and fail to deliver consistently high performance.

The real-time estimation of time-varying parameters from high-dimensional, heavy-tailed and corrupted data-streams is a common sub-routine in systems ranging from those for network monitoring and anomaly detection to those for traffic scheduling in data-centers.